TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

Journal: Bioinformatics (Oxford, England)
PMID: 39880376

Abstract

MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding patterns between TCRs and peptides is critical for advancing these clinical applications. While several computational tools have been developed, they neglect the directional semantics inherent in sequence data, which are essential for accurately characterizing TCR-peptide interactions.

Authors

  • Meng Wang
    State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150001, China.
  • Wei Fan
    Department of Epidemiology, School of Public Health, Soochow University, Suzhou 215123, China.
  • Tianrui Wu
    School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.

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